Large-Scale Recommender Systems Engineer

TikTok USDS Joint Venture; Seattle (WA); On-site; USD 130,000 - 246,000; Full time

Job summary

TikTok USDS Joint Venture LLC is seeking applied ML engineers and data scientists focused on feed and E-commerce recommendations. We develop innovative algorithms to improve user engagement and satisfaction, turning creative ideas into business-impacting solutions.

The team works on large-scale machine learning to solve real-world problems. This role involves building large-scale recommender systems, developing user-interest models, and iterating on predictive models for ranking.

Qualifications

• Bachelor's degree or higher in Computer Science or related fields.
• Strong programming and problem-solving ability.
• Experience in applied machine learning with common algorithms.
• Experience with deep learning frameworks such as TensorFlow or PyTorch.
• Experience with C++ or Python or equivalent.

Responsibilities

• Participate in building large-scale recommendation algorithms and systems.
• Build long and short term user interest models, analyze and extract relevant information from large amounts of data and design algorithms to explore users' latent interests efficiently.
• Design, develop, evaluate and iterate on predictive models for candidate generation and ranking, including real-time data pipelines, feature engineering, model optimization and innovation.
• Design and build supporting/debugging tools as needed.

Skills

Strong programming

Applied ML experience

ML algorithms exposure

Deep Learning (TF/PyTorch)

C++/Python

Education

Bachelor's degree in Computer Science or related fields

Tools

TensorFlow

PyTorch

Job description

TikTok USDS Joint Venture LLC is seeking applied ML engineers and data scientists focused on feed and E-commerce recommendations. We develop innovative algorithms to improve user engagement and satisfaction, turning creative ideas into business-impacting solutions.

The team works on large-scale machine learning to solve real-world problems. This role involves building large-scale recommender systems, developing user-interest models, and iterating on predictive models for ranking.